Customised in vitro model to detect human metabolism-dependent idiosyncratic drug-induced liver injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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A case report of a drug‐induced liver injury (DILI) caused by multiple antidepressants with causality established by the updated Roussel Uclaf causality assessment method (RUCAM) and in vitro testing

Clinical Case Reports ◽

10.1002/ccr3.3348 ◽

2020 ◽

Vol 8 (12) ◽

pp. 3105-3109

Author(s):

Miguel González‐Muñoz ◽

Jaime Monserrat Villatoro ◽

Eva Marín‐Serrano ◽

Stefan Stewart ◽

Belén Bardón Rivera ◽

...

Keyword(s):

Case Report ◽

Liver Injury ◽

Causality Assessment ◽

Assessment Method ◽

In Vitro Testing ◽

Drug Induced ◽

Drug Induced Liver Injury

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The cardiac work-loop technique: An in vitro model for identifying and profiling drug-induced changes in inotropy using rat papillary muscles

Scientific Reports ◽

10.1038/s41598-020-58935-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sophie Fletcher ◽

Helen Maddock ◽

Rob S. James ◽

Rob Wallis ◽

Mayel Gharanei

Keyword(s):

In Vitro Model ◽

Papillary Muscles ◽

Cardiac Work ◽

Drug Induced ◽

Loop Technique ◽

Vitro Model ◽

Induced Changes ◽

Work Loop

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

International Journal of Molecular Sciences ◽

10.3390/ijms21062114 ◽

2020 ◽

Vol 21 (6) ◽

pp. 2114

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

Toxicology Research ◽

10.1039/c8tx00016f ◽

2018 ◽

Vol 7 (3) ◽

pp. 358-370 ◽

Cited By ~ 11

Author(s):

Rosa Chan ◽

Leslie Z. Benet

Keyword(s):

Liver Injury ◽

Drug Development ◽

Safety Concern ◽

Drug Induced ◽

Development Review ◽

Drug Induced Liver Injury ◽

Underlying Mechanisms ◽

Early Drug

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated.

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The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

Archives of Toxicology ◽

10.1007/s00204-020-02763-w ◽

2020 ◽

Vol 94 (8) ◽

pp. 2559-2585 ◽

Cited By ~ 3

Author(s):

Paul A. Walker ◽

Stephanie Ryder ◽

Andrea Lavado ◽

Clive Dilworth ◽

Robert J. Riley

Keyword(s):

Drug Discovery ◽

Liver Injury ◽

Drug Uptake ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

New Chemical Entities ◽

Preclinical Animal Models ◽

Uptake Transporters

Abstract Early identification of toxicity associated with new chemical entities (NCEs) is critical in preventing late-stage drug development attrition. Liver injury remains a leading cause of drug failures in clinical trials and post-approval withdrawals reflecting the poor translation between traditional preclinical animal models and human clinical outcomes. For this reason, preclinical strategies have evolved over recent years to incorporate more sophisticated human in vitro cell-based models with multi-parametric endpoints. This review aims to highlight the evolution of the strategies adopted to improve human hepatotoxicity prediction in drug discovery and compares/contrasts these with recent activities in our lab. The key role of human exposure and hepatic drug uptake transporters (e.g. OATPs, OAT2) is also elaborated.

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Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline

Toxicological Sciences ◽

10.1093/toxsci/kfaa094 ◽

2020 ◽

Vol 177 (1) ◽

pp. 121-139 ◽

Cited By ~ 4

Author(s):

Wen Kang ◽

Alexei A Podtelezhnikov ◽

Keith Q Tanis ◽

Stephen Pacchione ◽

Ming Su ◽

...

Keyword(s):

Liver Injury ◽

Human Model ◽

Reactive Metabolites ◽

In Vitro Test ◽

Drug Induced ◽

Vitro Assay ◽

Drug Induced Liver Injury ◽

Drug Candidates ◽

Transcriptomic Signature

Abstract Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.

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